Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaign

Evaluating high-resolution forecasts of atmospheric CO and CO2

Evaluating high-resolution forecasts of atmospheric CO and CO2 from a global prediction system during KORUS-AQ field campaignEvaluating high-resolution forecasts of atmospheric CO and CO2Wenfu Tang et al.

Accurate and consistent monitoring of anthropogenic combustion is
imperative because of its significant health and environmental impacts,
especially at city-to-regional scale. Here, we assess the performance of the
Copernicus Atmosphere Monitoring Service (CAMS) global prediction system
using measurements from aircraft, ground sites, and ships during the
Korea-United States Air Quality (KORUS-AQ) field study in May to June 2016. Our
evaluation focuses on CAMS CO and CO2 analyses as well as two
higher-resolution forecasts (16 and 9 km horizontal resolution) to assess their
capability in predicting combustion signatures over east Asia. Our results
show a slight overestimation of CAMS CO2 with a mean bias against
airborne CO2 measurements of 2.2, 0.7, and 0.3 ppmv for 16 and
9 km CO2 forecasts, and analyses, respectively. The positive
CO2 mean bias in the 16 km forecast appears to be consistent
across the vertical profile of the measurements. In contrast, we find a
moderate underestimation of CAMS CO with an overall bias against airborne CO
measurements of −19.2 (16 km), −16.7 (9 km), and −20.7 ppbv
(analysis). This negative CO mean bias is mostly seen below 750 hPa for all
three forecast/analysis configurations. Despite these biases, CAMS shows a
remarkable agreement with observed enhancement ratios of CO with
CO2 over the Seoul metropolitan area and over the West (Yellow) Sea, where
east Asian outflows were sampled during the study period. More efficient
combustion is observed over Seoul
(dCO/dCO2=9 ppbv ppmv−1) compared to the West Sea
(dCO/dCO2=28 ppbv ppmv−1). This
“combustion signature contrast” is consistent with previous studies in
these two regions. CAMS captured this difference in enhancement ratios
(Seoul: 8–12 ppbv ppmv−1, the West Sea: ∼30 ppbv ppmv−1)
regardless of forecast/analysis configurations. The correlation of CAMS CO
bias with CO2 bias is relatively high over these two regions
(Seoul: 0.64–0.90, the West Sea: ∼0.80) suggesting that the contrast
captured by CAMS may be dominated by anthropogenic emission ratios used in
CAMS. However, CAMS shows poorer performance in terms of capturing
local-to-urban CO and CO2 variability. Along with measurements at
ground sites over the Korean Peninsula, CAMS produces too high CO and
CO2 concentrations at the surface with steeper vertical gradients
(∼0.4 ppmv hPa−1 for CO2 and 3.5 ppbv hPa−1 for
CO) in the morning samples than observed (∼0.25 ppmv hPa−1 for
CO2 and 1.7 ppbv hPa−1 for CO), suggesting weaker boundary
layer mixing in the model. Lastly, we find that the combination of CO
analyses (i.e., improved initial condition) and use of finer resolution
(9 km vs. 16 km) generally produces better forecasts.

Anthropogenic combustion significantly impacts air quality, climate,
ecosystem, agriculture, and public health at local to global scales (Charlson
et al., 1992; Doney et al., 2007;
Feely et al., 2004; Heald et al., 2006; Maher et al., 2016). This is
especially the case in megacities where human activities are most intense,
accompanied by immense energy consumption, mainly in the form of fossil-fuel
combustion, which directly leads to enhanced emissions of air pollutants,
greenhouse gases, and waste energy. In particular, cities in the Asian region
that are rapidly developing in recent decades are subject to more frequent
severe pollution conditions (Yang, 2013; Guo et al., 2014; Ohara et al., 2007; Shindell et al., 2008,
2011). It is imperative therefore that we enhance our current capability to
monitor, verify, and assess anthropogenic combustion and its impacts as the
number of megacities across the globe is expected to rapidly grow in the
following decades (United Nations, 2016). The Copernicus Atmosphere
Monitoring Service (CAMS) has a state-of-the-art global and integrated prediction
system that is currently being implemented to meet this need. The service is
funded by the European Union and it builds upon a legacy of projects such as
the Monitoring Atmospheric Composition and Climate (MACC) and Global and
Regional Earth System Monitoring Using Satellite and In Situ Data (GEMS)
(Hollingsworth et al., 2008).

Table 1Configuration of CAMS global atmospheric composition products valid
during the period of the Korea-United States Air Quality (KORUS-AQ) field campaign (May to June 2016). The
tracers evaluated in this paper are highlighted in boldface. Time
availability is in number of days with respect to real time
(n/a is
used when this is not applicable).

For nearly a decade, CAMS has been operationally producing daily global
near-real-time forecasts and analyses of reactive trace gases, greenhouse
gases, and aerosols including global reanalyses and estimation of emissions
of these atmospheric constituents (Morcrette et al., 2009; Benedetti et al.,
2009; Kaiser et al., 2012; Flemming et al., 2015, 2017;
Massart et al., 2016; Agustí-Panareda et al., 2014, 2017). CAMS global forecasts and analyses are based on the Integrated
Forecasting System (IFS) of the European Centre for Medium-Range Weather
Forecasts (ECMWF), which is also used for numerical weather prediction
(NWP). CAMS recently developed two forecasts at higher resolution, which have
potential advantages compared to lower-resolution analysis and/or forecast,
in terms of local-to-regional air quality (Table 1).

The Korea-United States Air Quality (KORUS-AQ) field measurement campaign
offers a unique opportunity to assess the accuracy and consistency of the
high-resolution forecast and analysis system of CAMS and its skill in
simulating atmospheric CO2 from anthropogenic combustion. During
May to June 2016, the KORUS-AQ field campaign collected comprehensive
measurements of air quality (including CO2 and tracers of
fossil-fuel combustion) over the South Korean peninsula and its surrounding
waters. KORUS-AQ is an international collaboration between the US and South
Korea to better understand the factors controlling air quality in the region
across urban, rural, and coastal interfaces (Kim and Park, 2014, KORUS-AQ
White Paper). This field campaign follows several NASA-led suborbital
missions in the past focusing on air quality in the United States (e.g.,
DISCOVER-AQ, SEAC4RS) and pollution outflows from Asia (e.g., TRACE-P,
INTEX-B, ARCTAS), and integrating the measurements from these campaigns to
satellite retrievals and air quality models (Crawford and
Pickering, 2014; Toon et al., 2016; Jacob
et al., 2003, 2010; Singh et al., 2009). Local measurements over the West (Yellow)
Sea, often representative of Chinese pollution outflow, and over the Seoul
metropolitan area provide a rich dataset that is very useful in evaluating
global prediction and analysis systems like CAMS at city-to-regional scale.

In this study, we evaluate CAMS forecast and analysis of fossil-fuel
combustion signatures over the KORUS-AQ spatial and temporal domain. In
particular, we use measurements of the main products of combustion (i.e., CO
and CO2; Gamnitzer et al., 2006) from the NASA DC-8 aircraft, along with observations from five
ground sites, two research ships, and four satellites to assess the
capability of CAMS to monitor anthropogenic combustion. Although CAMS CO and
CO2 forecasts and analyses have been evaluated previously
(Agustí-Panareda et al., 2014, 2016, 2017; Claeyman et al., 2010; Massart et al.,
2016; Flemming et al., 2009, 2015, 2017),
this study is unique for the following reasons. (1) This study is a joint
evaluation of CO and CO2 species, including their associated
enhancement ratios which provide insight on CAMS representation of
anthropogenic combustion processes. (2) A focus on megacities provides an
important baseline investigation. This is especially the case in east Asia
where there is still lack of detailed information and measurements to
constrain emission inventories. (3) KORUS-AQ provides a unique opportunity
to evaluate the new high-resolution global CAMS forecasts of CO and CO2
at local-to-regional scale. This paper begins with a brief description of
CAMS and KORUS-AQ (Sect. 2), followed by an evaluation of CAMS with
airborne measurements (Sect. 3) and with ground sites, ships, and
satellites (Sect. 4). We provide a summary of our findings in Sect. 5.

2.1 CAMS CO and CO2 forecasts and analysis

CAMS has been providing global
forecasts and analysis of atmospheric composition on a daily basis at ECMWF
for nearly a decade with applications on air quality and monitoring of
long-lived greenhouse gases. CAMS uses the IFS for NWP to assimilate a wealth of
meteorological observations plus satellite products of atmospheric
composition to produce atmospheric analysis of reactive gases (e.g., CO,
O3, NO2, SO2), aerosols, and long-lived
greenhouse gases (e.g., CO2, CH4) on the NWP model grid
which are then used as initial conditions to forecast the atmospheric
composition with a 5-day lead time. The IFS simulates transport of the
chemical species (Flemming et al., 2009; Agustí-Panareda et al., 2017)
and includes the online integration of modules for atmospheric chemistry
(Flemming et al., 2015, 2017) and biogenic CO2 fluxes from
terrestrial vegetation (Boussetta et al., 2013) to model atmospheric
composition in conjunction with an assimilation system based on
four-dimensional variational (4D-Var) data assimilation (Rabier et al., 2000;
Inness et al., 2015). The CAMS global atmospheric analysis and prediction
system runs at different resolutions and at a different lag times for the
various atmospheric species depending on the use of chemistry in the model
and the timeliness of the satellite retrievals used in the analysis. The
system providing reactive trace gases and aerosols runs at approximately
80 km horizontal resolution with 60 vertical levels, and its analysis is
available less than 1 day behind real time. While higher horizontal and
vertical resolution are used for the analysis and forecasts of greenhouse
gases, the analysis of CO2 and CH4 is available at around
40 km in the horizontal and 137 vertical levels. Currently, the forecasts of
CO2 and CH4 have the same resolution as the operational
weather forecast at ECMWF (137 levels with 9 km horizontal resolution) but
previously their resolution was 16 km (from 2015 to 2016). A CO tracer with
simplified chemistry based on a linear CO scheme (Massart et al., 2015) is
also available in the high-resolution forecasts. However, the CO2
and CH4 analysis is only available 4 days behind real time as
the satellite retrievals are not available closer to real time. Because of
this, in the 16 km resolution forecast, CO2, CH4, and
linear CO are free running, and only the meteorology is initialized with the
meteorological operational analysis (see Agustí-Panareda et al., 2014
for further details on the free-running forecast configuration). Following a
recent improvement in the timeliness of the satellite retrievals, the linear
CO is initialized with CO analysis, while CO2 and CH4 are
initialized with a 4-day forecast from the CO2 and CH4
40 km analysis in the 9 km forecasts. In order not to lose the small-scale
features in the initialization process, a spectral filter is applied to only
adjust the large scales in the initial conditions of the forecast
(Sebastien Massart, personal communication,
2016). Table 1 (as well as Fig. S1 in the Supplement) provides a summary of
the three CAMS configurations and five resulting CAMS products evaluated in
this paper and Fig. S2 depicts the different vertical and horizontal
resolutions used in the different CAMS configurations.

For this study, we focus on evaluating the three CO and CO2 forecasts
and analysis products listed above, namely, CO2 and CO 16 km
forecasts (FC16s), analyses (ANs) of CO2 (at 40 km) and CO (at 80 km), and
relatively recent CAMS 9 km CO2 and CO forecast products (FC9s) which
are initialized from their respective analysis. The FC9s are different from
FC16s in terms of both resolution and initialization as described above
(e.g., the FC16s are produced from a free-running simulation of CO2 and
CO). The near-real-time ANs of CO and CO2 are also different from FC16s
and FC9s as these ANs continuously assimilate satellite retrievals of CO
total column from the Measurements Of Pollution In The Troposphere (MOPITT
V5-TIR) and the Infrared Atmospheric Sounding Interferometer (IASI) (Inness
et al., 2015), and column-averaged dry-air mole fractions of CO2
(XCO2) from the Greenhouse gases Observing Satellite (GOSAT) (Massart
et al., 2016), in addition to the available meteorological data.
Observations of both CO and CO2 are assimilated in 12 h assimilation
windows. Inness et al. (2015) found that CO total column field, vertical
distribution, and concentrations in the lower troposphere are improved by
assimilating the CO total column from MOPITT. Assimilation of the GOSAT
XCO2 led to improvements in mean absolute error and bias variability
in XCO2 fields during the year 2013 (Massart et al., 2016). FC9s CO are
initialized from MOPITT and IASI CO analysis at a previous time, which are
then downscaled from 80 km to 9 km by a spectral filtering scheme. Due to
observational and computing constraints, FC9s of CO2 are initialized
and downscaled from a 96 h forecast of CO2 initialized by GOSAT
analysis 4 days earlier.

The IFS contains several components, including an atmospheric general
circulation model, a land surface model, an ocean wave model, an ocean
general circulation model, and perturbation models for the data assimilation
and forecast (Persson, 2001). Model dynamics and numerical procedures, and
physical processes are documented in IFS documentation Cy43r3 (ECMWF, 2017;
https://www.ecmwf.int/search/elibrary/part?title=part&year=2017&secondary_title=IFS,
last access: 15 May 2018). Detailed cloud and precipitation physics of the
IFS benefits the calculation of wet deposition (Flemming et al., 2017). As
for emissions and surface fluxes, CAMS uses the Global Fire Assimilation
System (GFAS) for biomass burning fluxes of CO2 (Kaiser et al.,
2012). CAMS uses the anthropogenic CO2 fluxes that are based on the
annual mean of the Emission Database for Global Atmospheric Research
version 4.2 (EDGARv4.2). As the most recent year available for EDGARv4.2 is
2008, estimated and climatological trends are used to extrapolate to the
years after 2008. The land vegetation fluxes for CO2 are calculated
online by the carbon module of the land surface model in IFS CTESSEL
(Boussetta et al., 2013). A biogenic flux adjustment scheme (BFAS) is
employed in CAMS to improve the continental budget of CO2 fluxes
(Agustí-Panareda et al., 2014, 2016). Specifically, (1) BFAS computes
the scaling factors for the model net ecosystem exchange (NEE) based on
reference (NEE climatology from the optimized fluxes); (2) the scaling
factors are used to adjust biogenic CO2 fluxes from the land
surface model (i.e., flux bias correction); (3) the bias-corrected fluxes are
then used to simulate the atmospheric CO2. According to
Agustí-Panareda et al. (2016), in northern Asia, the employment of BFAS
slightly decreases NEE in May and has negligible impacts on NEE in June.
CO2 overestimation by CAMS over the Northern Hemisphere (NH) in
winter and spring is enhanced by BFAS. For CO, CAMS uses anthropogenic and
biogenic emissions that are based on the MACC/CityZEN EU projects (MACCity)
(Granier et al., 2011), and a climatology of the Model of Emissions of Gases
and Aerosols from Nature developed under the MACC (MEGAN-MACC) emission
inventories (Sindelarova et al., 2014). GFAS is also used for fire emissions.
ANs for CO use the online implemented chemical mechanism (C-IFS-CB05;
Flemming et al., 2015) that is an extended version of the Carbon Bond
mechanism 5 (CB05; Yarwood et al., 2005). Because hydroxyl radical (OH) is an
important sink for CO, modeled OH is critical for the simulation of CO
(Gaubert et al., 2016, 2017). In the ANs for CO, the global and NH means of
air mass-weighted OH are 0.98×10-6 and 1.20×10-6 molecules cm−3 during May 2016, respectively (calculated
following recommendations from Lawrence et al., 2001). The mean OH from the
ANs for CO is consistent with previous studies (e.g., Lawrence et al., 2001;
Lelieveld et al., 2016; Gaubert et al., 2016, 2017). A linear chemistry
scheme (C-IFS-LINCO) is used in
FC16s and FC9s for CO for computational expediency (Claeyman et al., 2010;
Massart et al., 2015; Eskes et al., 2017). C-IFS-LINCO
computes CO sources and sinks using the approach developed by Cariolle and
Déqué (1986) and updated by Cariolle and Teyssèdre (2007),
without direct use of modeled OH. C-IFS-LINCO is less computationally
demanding than the full chemistry, permitting simulations at higher
resolutions (Massart et al., 2015). Key aspects of the three CAMS
configurations evaluated in this study are listed in Table 1.

Figure 1Domain of the study and KORUS-AQ
measurements used in this study. Panel (a) shows land cover of the
domain (Broxton et al., 2014), DC-8 aircraft tracks, ship tracks, and
location of ground sites. The airborne measurements are classified into
five groups (the West (Yellow) Sea, Seoul, Taehwa, Seoul–Jeju jetway, and
Seoul–Busan jetway), as marked in bright green, bright blue, mazarine blue,
orange, and magenta. The ground sites are labeled with bright yellow markers.
The Olympic Park and Yonsei sites are located in urban regions (Seoul) while
the Baengnyeong and Fukue (Kanaya et al., 2016) sites are located in remote
regions. The Taehwa (Kim et al., 2013) site is located in a forest near
Seoul. Tracks of the two ships are marked in dark grey (R/V Jangmok)
and light grey (R/V Onnuri). Also shown in panel (b) is the
zoomed-in version of the grey box in panel (a). Panel (c)
shows a composite MOPITT XCO retrievals during KORUS-AQ campaign while
panel (d) shows OCO-2 XCO2 retrievals in the same time
period.

2.1.1 CO and CO2 measurements during KORUS-AQ

KORUS-AQ is a comprehensive field campaign based on international
collaboration between the US and South Korea
(https://espo.nasa.gov/korus-aq, last access: 10 June 2018). The goal
is to better understand the factors controlling air quality (AQ) in the
region across urban, rural, and coastal interfaces. The field campaign was
conducted over the South Korean peninsula and surrounding waters from May to
June 2016. The South Korean peninsula and its surrounding waters are a
desirable region to conduct the campaign because (1) South Korea's
urban/rural sectors are distinct, which is advantageous for distinguishing
anthropogenic and natural emissions; (2) South Korea is embedded in a rapidly
changing region; (3) the region allows studies of local versus transboundary
pollution; and (4) air quality monitoring and ground-based measurements are
provided by South Korea. AQ measurements (including CO2) from
aircraft, ships, and ground sites were obtained during this period. The
campaign was designed to answer three scientific
questions. (1) What are the challenges and opportunities for satellite observations of air quality?
(2) What are the factors governing ozone photochemistry and aerosol evolution?
(3) How well do models perform, and what improvements are needed to better represent
atmospheric composition over South Korea and its connection to the larger
global atmosphere (Kim and Park, 2014, KORUS-AQ White Paper)?

Figure 1 shows the study domain (30–39∘ N,
123–133∘ E) along with the tracks from DC-8 aircraft
flights and research ship deployments. The locations of ground sites are
also added in Fig. 1. Satellite retrievals from MOPITT CO and Orbiting
Carbon Observatory-2 (OCO-2) CO2 are shown in Fig. 1 to provide spatial
context and coverage of remote sensing measurements during the campaign. All
the observational data used in this study are summarized in Table 2.

2.1.2 Airborne CO and CO2 measurements

We use measurements of CO2 and CO from the DC-8 aircraft.
CO2 was measured by Atmospheric Vertical Observations of
CO2 in the Earth's Troposphere (AVOCET) using a modified LI-COR
model 6252 non-dispersive infrared spectrometer (NDIR). This instrument
provides CO2 concentrations with high precision by sensing the
difference in light absorption between the continuously flowing sample and
reference gases (Vay et al., 2003, 2011;
https://airbornescience.nasa.gov/instrument/AVOCET, last access:
10 June 2018). CO2 1 Hz 1σ precision and accuracy are ±0.1 ppm and ±0.25 ppm, respectively. CO was measured by the
Differential Absorption CO Measurement (DACOM) instrument via infrared
wavelength modulation spectroscopy. The system uses three tunable diode
lasers providing 4.7, 4.5, and 3.3 µm radiation for accessing
absorption lines of CO, N2O, and CH4. The time response
for CO measurements is 1 s; the precision is < 1 % or
0.1 ppbv; the accuracy is 2 % (Warner et al., 2010;
https://airbornescience.nasa.gov/instrument/DACOM, last access: 10 June
2018). Calibrations for both instruments were performed during flight at
regular intervals using gas standards traceable to the WMO scale
(CO2: x2012; CO: x2008) and certified by the National Oceanic and
Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL).
Details about the two instruments are listed in Table 2. Note that we use the
1 min (60 s) merged DC-8 data in this study. The data are available at the
NASA Langley Research Center archive
(https://www-air.larc.nasa.gov/missions/korus-aq/, last access: 10 June
2018).

There were 20 formal DC-8 science flights. Note that for time reference, the
“date” in this paper refers to the day on which the flight started in UTC
time instead of South Korean local time, unless the term “local time” is
explicitly used. This “date” in UTC time is 1 day behind South Korean local
time as all flights typically start at 08:00 LT. We also divide the flight
measurements into five groups based on the land cover below the flight tracks
and types of pollution sources with which they can be broadly associated.
These groups are classified as the Seoul metropolitan area, Taehwa, the West
(Yellow) Sea, Seoul–Jeju jetway, and Seoul–Busan jetway (please refer to
Fig. 1 for an illustration of these flight groups). The Seoul metropolitan
area represents air samples over the large city of Seoul which can have a dominant
signature from anthropogenic combustion processes. On the other hand, Taehwa
represents air samples over a forest area near Seoul, which can be influenced
by both surface carbon fluxes from the local forest as well as anthropogenic
emissions from Seoul. Measurements over the West Sea were designed to capture
China pollution outflows. The flight tracks over the West Sea were typically
zonal tracks forming a “wall” between China and South Korea (see Fig. 1).
These flights are conducted only when a China outflow is expected to be
present based on weather and AQ forecasts during the campaign. These
measurements enable us to investigate combustion signatures from China and
differentiate them from Seoul. The Seoul–Jeju jetway and Seoul–Busan jetway
groups are two jetway flights on which the DC-8 aircraft frequently obtain
measurements. The two jetways are both above the Korean Peninsula, connecting
Seoul to Jeju and Busan, respectively. Flights in the Seoul–Busan jetway are
designed to capture activities in forest, rural, and Busan urban regions. The
flights in the Seoul–Jeju jetway, on the other hand, sample air over local power
plants, transported air from the West Sea, and over nearby croplands. We will
discuss our CAMS evaluation for each of these five groups in Sect. 3.

2.1.3 Ground-based CO and CO2 measurements

Observations from the following ground sites are used for comparison with
CAMS CO and CO2: Baengnyeong, Fukue, Olympic Park, Taehwa, and Yonsei
University (see Fig. 1 for the site locations). The sites in Baengnyeong and
Taehwa are managed by the National Institute of Environmental Research
(NIER). The Baengnyeong site is located on the sparsely populated
Baengnyeong Island, Incheon, northwest of Seoul. The Fukue site belongs to
the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and is
located on the remote island of Fukue, Japan (Kanaya et al., 2016). The
Olympic Park and Yonsei University sites belong to Korea Research Institute
of Standards and Science and Yonsei University, respectively. Both sites are
located within the Seoul metropolitan area. These five ground sites cover
different environments, which allows us to differentiate between urban
(Olympic Park and Yonsei University) and remote (Baengnyeong and Fukue) air
quality conditions during the campaign. The sites in Baengnyeong, Fukue, and
Olympic Park provide measurements of CO (in ppbv), while the site at Yonsei
University provides measurements of CO2 (in ppmv). Only the site in
Taehwa provides measurements of both CO (in ppbv) and CO2 (in
mg m−3) (Kim et al., 2013). Locations of the five sites, and
corresponding instruments and data intervals are provided in Table 2.
Note that we use data from these sites taken during the KORUS-AQ campaign
period to provide the ground context of our evaluation.

2.1.4 Ship observations

We use ship measurements of CO from R/Vs Jangmok and
Onnuri. Both of them are research vessels owned by Korea Institute
of Ocean Science and Technology. The ship deployments are part of the
Korea-United States Ocean Color (KORUS-OC) field study coinciding with
KORUS-AQ. KORUS-OC was led by NASA and the Korean Institute of Ocean Science
and Technology, focusing on the ocean color, biology, and biogeochemistry as
well as atmospheric composition in coastal waters adjacent to South Korea
(https://www.asp.ucar.edu/sites/default/files/4_Emmons_07_27_2016.pdf,
last access: 10 June 2018). The two ships sailed along the South Korean coast
from 20 May to 5 June. Tracks of the two ships are shown in Fig. 1 in dark
grey (Jangmok) and light grey (Onnuri). CO measurements on
R/Vs Jangmok and Onnuri were taken from the Thermo 48i-TLE
CO analyzer and Thermo 48C CO analyzer, respectively
(http://www.kiost.ac.kr/kor.do, last access: 10 June 2018), and are
provided every minute.

2.1.5 Satellite-derived CO and CO2 retrievals

We use four sets of satellite-derived measurements for comparison with CAMS
CO and CO2. We use retrievals of CO2 column-averaged
dry-air mole fraction (XCO2) from NASA OCO-2, version 7, level 2
(L2) full product with the standard quality flag and warn level ≤15
(Crisp et al., 2004; Boesch et al., 2011; Wunch et al., 2011a, b, 2017;
Osterman et al., 2016; Mandrake et al., 2015;
https://oco.jpl.nasa.gov/, last access: 10 June 2018), and from the
Japan Aerospace Exploration Agency (JAXA) GOSAT, level 2 (L2), version 2
(Yokota et al., 2004, 2009; Morino et al., 2011; Crisp et al., 2012;
http://global.jaxa.jp/projects/sat/gosat/, last access: 10 June 2018).
Short-wavelength infrared observations measured by the Thermal And
Near-infrared Sensor for carbon Observation (TANSO) aboard the GOSAT
satellite are used to retrieve XCO2. OCO-2 also has three specific
near-infrared (NIR) wavelength bands to retrieve XCO2
(https://oco.jpl.nasa.gov/). For CO, we use the NASA Terra MOPITT
version 6, level 2, multispectral (thermal infrared/near infrared; TIR/NIR)
total column retrievals (MOP02J, L2, V6) with the standard quality flag.
Compared to thermal-infrared-only retrievals (TIR), these retrievals have an
enhanced sensitivity to the lower tropospheric CO (Deeter et al., 2014;
https://www2.acom.ucar.edu/mopitt, last access: 10 June 2018). In
addition, we also use total column mole fractions of CO from IASI level 2
data with the standard quality flag (George et al., 2009; Clerbaux et al.,
2009). IASI is aboard MetOp-A and B satellites and uses Fast Optimal
Retrievals on Layers for IASI (FORLI) to retrieve CO distributions from the
TIR spectra. We applied the associated averaging kernels from MOPITT and IASI
to CAMS CO before comparison as these retrievals exhibit large sensitivities
in the free troposphere. We also note that both IASI and MOPITT have
significantly more observations than OCO-2 and GOSAT. As summarized in
Table 2, the resolutions of OCO-2, GOSAT, MOPITT, and IASI are 2.25×1.29, 10.5×10.5, 22×22, and 12×12 km, respectively.
The overpass times for the four satellites are also different. OCO-2
overpasses at 13:18–13:33 LT, GOSAT overpasses at around 13:00 LT.
Overpass time is 10:30 LT for MOPITT, and 09:30 LT for IASI. Uncertainties
have also been reported for these satellite products. OCO-2 XCO2
has uncertainties of 1–2 ppm (Boesch et al., 2011) while GOSAT
XCO2 has retrieval errors of 2 ppm (Morino et al., 2011; Crisp et
al., 2012). Deeter et al. (2014) reported 0.09×1018 molecules cm−2 for total column retrieval for MOPITT.
De Wachter et al. (2012) reported uncertainties to be < 13 % for
IASI FORLI.

Here, we evaluate CAMS forecasts and analysis of CO and CO2 with NASA
DC-8 aircraft observations. We interpolate the 4-D fields of CAMS CO and
CO2 model output to collocate with flight measurements in both space
and time. The equivalent model data for all flights and for the three
configurations (FC16s, FC9s, ANs) are made available in the same file format
as the 1 min merged DC-8 dataset to facilitate model-to-observation
comparison. We also estimate enhancement ratios of CO and CO2 from both
airborne and model data and analyze their spatial and temporal variations
across different flights. We present in the following subsections the
summary statistics of our comparison of CAMS data with the DC-8 aircraft
data.

3.1 Performance across all flights

Across all flight data, CAMS overestimates CO2, with mean biases of
2.2, 0.7, and 0.3 ppmv for FC16s, FC9s, and ANs, respectively.
Agustí-Panareda et al. (2016) also suggested CO2 is
overestimated by CAMS in the NH at the end of winter and throughout spring.
In contrast, CAMS underestimates CO with mean biases for FC16s, FC9s, and ANs
against the DC-8 aircraft data of −19.2, −16.7, and −20.7 ppbv,
respectively. The mean bias is calculated as the average across all data of
CAMS minus the DC-8 aircraft data. We also find that the overall pairwise
correlation between the DC-8 aircraft data and CAMS is moderately high
(CO2: 0.52–0.57, CO: 0.65–0.73) while the root mean square errors
(RMSEs) in CAMS relative to the DC-8 aircraft data are about 7 ppmv for
CO2 and 80 ppbv for CO. These statistics can be summarized using a
Taylor diagram as shown in Figs. S3 and S4 of the Supplement. We also
calculated the associated Taylor scores to summarize the skill of CAMS in
capturing the observed CO2 or CO variations. The Taylor score (Taylor,
2001) is defined by

(1)S=4(1+R)σ^f+1/σ^f21+R0,

where σ^f is the ratio of σf
(standard deviation of the model) and σr (standard deviation of observations), R is the correlation
between model and observations, and R0 is the maximum
potentially realizable correlation (equivalent to 0.9 in this study).

Figure 2Box plot for each individual flight. The flight date (MDD) for each
box plot is indicated in the bottom x axis. Note that the dates here are in
UTC time instead of South Korean time. Panel (a) is for CO2 and
panel (b) is for CO. The first row corresponds to the box plot of the
abundances measured by DC-8 aircraft. The second, third, and fourth rows
correspond to the box plot of the bias of FC16s, ANs, and FC9s relative to the
DC-8 aircraft data, respectively. The purple shade marks the flights with
frontal passage, and orange shade marks the flights that may possibly be
affected by biomass burning. The grey shade marks the flight measuring China
outflow while yellow shade marks the flight surveying point emission
sources.

We find that CAMS has relatively good skill regardless of configuration: for
CO2, the skill scores are 0.82 (FC16s), 0.82 (FC9s), and 0.75
(ANs), while for CO, the skill scores are 0.85 (FC16s), 0.86 (FC9s), and 0.83
(ANs). However, it is important to note that these statistics can vary from
flight to flight and the skill for CO2 is not necessarily related to
that of CO. For instance, for the 10 May flight, where a southern
peninsula outflow was expected, CAMS ANs show higher skill than those from
FC9s in terms of both CO2 and CO, while the scores of FC16s are higher
than those of FC9s in terms of CO (Fig. S5). Yet, for the 3 May
flight, where a weak Chinese influence was expected, the scores of FC16s and
FC9s are higher for CO2 than CO, while we find the opposite for the
2 June flight, where the DC-8 aircraft sampled local
influences. Lastly, we note that the skill of CAMS during the 4 June flight is not high for either species. This flight was designed
to measure local point sources with large variations at much finer scales.

3.2 Performance across individual flights

We present in Fig. 2 the summary statistics of CAMS against the DC-8 aircraft
data for all 20 individual flights. This is shown in the second to fourth
rows of Fig. 2 as box plots of the bias for FC16s, ANs, and FC9s,
respectively. We also show the box plot of the airborne measurements of
CO2 (first row, left column) and CO (first row, right column) for
each flight as points of comparison. The overall mean, median, interquartile
range (IQR), and standard deviation (σ) of the airborne measurements
of CO2 mixing ratios (in ppmv) are 410.37, 408.25, 5.97, and 7.73,
respectively. The overall mixing ratio, which varies within 1 to 2 %, is
slightly higher than the monthly median observed in Mauna Loa (NOAA,
https://www.esrl.noaa.gov/gmd/ccgg, last access: 10 June 2018) for May
2016 (408±1 ppmv). For the airborne measurements of CO mixing ratios
(in ppbv), the corresponding statistics (mean: 204.59, median: 183.90, IQR:
127.97, σ: 101.74) show enhanced CO (and larger variance) than the
background value observed in Mauna Loa (100±24 ppbv). In general, CAMS
overestimates CO2 and underestimates CO for most flights.
Differences also exist among the 20 flights in terms of both measured mixing
ratios and model biases from the DC-8 aircraft. For flights with higher
observed variances, CAMS biases and the corresponding variance of the biases
tend to be also larger. This is related to variations in weather conditions
during the campaign along with variations in sampling goals of the science
flights. For example, parts of flight tracks on 3, 17, 24, 29, and 30 May
were specifically designed to capture Chinese pollution outflow. In these
days, the variances in CAMS biases for CO (but not CO2) are
generally larger than the average, except for the flight tracks on 3 May when
Chinese influences were expected to be weak. The colored shades in Fig. 2
indicate flights for “special conditions”. The grey and yellow shades
indicate two special cases that we study in detail in later sections. In
particular, DC-8 flew a “wall” over the West Sea on 24 May to investigate
the transport of Chinese pollution. On 4 June, DC-8 flew near Seoul to
measure pollution from local point sources (e.g., power plants). The other
shades indicate that the flights were conducted during a frontal passage
(purple) and that the flights may possibly be affected by fires in Siberia
(orange). These flights were not further analyzed in this study since, for
example, the 26 May flight (with frontal passage influence), and the 17 and
19 May flights (with possible fire influence) do not clearly stand out from
the other flights (see Fig. 2).

3.3 Performance across flight groups

Figure 3Probability density functions (pdfs) of CO2 and CO for
each flight group. Solid lines are pdfs for each group while the dashed lines
are pdfs for all groups.

Here, we evaluate CAMS per flight group as described in Sect. 2.2.1. We show
in Fig. 3 the probability density functions (pdfs) of CO and CO2
for the DC-8 aircraft data and CAMS per flight group. The
pdfs of CAMS CO2 (which
exhibits a longer tail to higher values) show a general offset to higher
values relative to the DC-8 aircraft data (except for the West Sea). There is
a systematic overestimation of CAMS CO2 against the DC-8 aircraft
data. Accordingly, the “apparent local background” of CO2 (lower
tails of the pdfs) is relatively higher in CAMS than the DC-8 aircraft data. In contrast, CO is
underestimated in CAMS across all of the five groups. The pdfs of CO in CAMS
show a bimodal distribution (except in Taehwa and the West Sea) indicative of
two dominant AQ conditions sampled by DC-8 over this region. The shapes of
the CO pdfs of CAMS largely differ from those of the DC-8 aircraft data
(except in Taehwa). We see a higher frequency of occurrence of the two to
three modes in the West Sea in CAMS that is not apparent in the DC-8 aircraft
data, while the opposite is the case in Seoul–Busan. This suggests that the
underestimation of CO in CAMS may not be systematic or may be caused by
biases in CO background values. The pdf over the West Sea also shows that
CAMS underestimates (or even misses) the more elevated CO observed by the
DC-8 aircraft.

We further investigate the differences between CAMS and the DC-8 aircraft
data by looking at the bias in the mean profiles. We show in Fig. 4 the mean
profiles for all data and each individual group. We find that the overall
bias in CAMS CO2 is systematic and close to uniform across all
layers (FC16s: ∼2.2 ppmv, FC9s: ∼1 ppmv, and ANs: ∼0.8 ppmv). This overestimation is true for all flight groups except over
the West Sea. On the other hand, for CO, the overall bias in CAMS is mostly
evident in the lower troposphere (about −20 to 25 ppbv below 700 hPa).
This underestimation is especially the case over the West Sea and is
consistent with the pdfs in Fig. 3.

3.3.1 The Seoul metropolitan area and Taehwa

The airborne measurements over the Seoul metropolitan area were mostly during
frequent aborted landing maneuvers (i.e., missed approaches) over the Seoul
Air Base. More than 90 % of the measurements in this group
were taken below 850 hPa. Figure 3
shows that the performance of FC16s, FC9s, and ANs is alike over Seoul for
both CO and CO2, in contrast to the other four flight groups. Given
that the measurements over Seoul are dominated by boundary layer (BL) and
anthropogenic emissions in Seoul, the model performance over Seoul is most
likely to be driven by local emissions. We show in Fig. 5 the mean vertical
profiles over Seoul below 800 hPa. For CO2, FC9s profiles agree
well with the observations. This is not the case for CO, where FC16s, FC9s,
and ANs do not agree well with the DC-8 aircraft data, but with the bias in
ANs being relatively smaller. However, the near-surface temporal variations
(changes in the profile from morning to afternoon) observed by the DC-8
aircraft are captured by FC16s, FC9s, and ANs. It is worth noting that, over
Seoul, there is an abrupt change in the profile at around 925 hPa for both
CO and CO2 of the morning samples. Accordingly, CO is overestimated
below 925 hPa and underestimated above 925 hPa. These vertical gradients
below 925 hPa (i.e., change in mixing ratios divided by change in pressure)
in the averaged profiles of the DC-8 aircraft data CO2 and CO are
about 0.25 ppmv hPa−1 and 1.7 ppbv hPa−1, respectively. In
contrast, the gradients of CO2 in CAMS are 0.50 ppmv hPa−1
for FC16s, 0.34 ppmv hPa−1 for FC9s, and 0.45 ppmv hPa−1 for
ANs while the gradients of CO in CAMS are 4.2 ppbv hPa−1 for FC16s,
3.4 ppbv hPa−1 for FC9s, and 3.3 ppbv hPa−1 for ANs. It is
evident that these gradients (CO and CO2) regardless of CAMS
configuration are significantly steeper than observed. While in part this may
be attributed to overestimation of emissions during rush hours (and
nighttime) in Seoul along with model representativeness errors in the BL, we
attribute this steep gradient to a possible weaker BL mixing in CAMS since
there is an important contrast between near-surface CO (overestimation) and
CO aloft (underestimation) which cannot be explained by emissions alone. This
is not very apparent in CO2 since there is an overestimation of
background CO2 superimposed on this difference. In addition, given
the air traffic over the Seoul Air Base (where the DC-8 aircraft frequently
conducted missed approaches), emissions from airplanes may also contribute to
the model biases (Boschetti et al., 2015).

Figure 4Averaged vertical profiles of CO2 and CO mixing ratios
from the DC-8 aircraft data and CAMS for each flight group. Horizontal bars
correspond to the interquartile ranges (between 25th and 75th percentiles) of
the layer bin.

Figure 5Temporal variation of averaged vertical profiles of CO2
and CO mixing ratios from the DC-8 aircraft data and CAMS over the Seoul and
Taehwa flight groups. The first, second, and third columns are averaged
CO2 profiles for all day, morning (08:00–10:00 LT), and afternoon
(14:00–16:00 LT), respectively. Horizontal bars correspond to interquartile
ranges (between 25th and 75th percentiles) of the profiles. The fourth,
fifth, and sixth columns are the same as the first three columns but for CO.

In Taehwa, the differences between morning and afternoon samples are not as
large compared to the Seoul metropolitan area. The CO2 profiles
from ANs and FC9s are apparently closer to the DC-8 aircraft data than those
from FC16s. However, this difference is not obvious for the CO profiles. Note
that in the afternoon (14:00–16:00 LT), measured CO2 mixing ratio
near the surface (at 975 hPa) becomes lower than the layer above, indicating
a possible drawdown of CO2 by underlying vegetation in Taehwa. This
change is captured by CAMS, especially in FC9s. We further find that compared
with the Seoul metropolitan area, the observed vertical gradient of
CO2 over Taehwa (∼0.03 ppmv hPa−1) below 925 hPa is
smaller, which is relatively better captured by CAMS
(0.02–0.12 ppmv hPa−1). This again implies the possible inefficient
BL mixing in CAMS over the Seoul urban environment. CO over Taehwa is more
likely to be due to regional transport, as Taehwa is not a strong CO source
region. Thus, the vertical gradient of CO over Taehwa does not necessarily
reflect the impact of BL mixing over Taehwa. We further compared the mixing
layer (ML) height derived from the KORUS-AQ Airborne Differential Absorption
Lidar – High Spectral Resolution Lidar (DIAL-HSRL) measurements of aerosol
backscatter following the technique from Brooks (2003), and the BL heights
from CAMS. We note that ML height is only approximately equal to BL height.
We find that CAMS generally underestimates BL heights during KORUS-AQ
(Fig. S6). The model underestimation of BL over the Seoul metropolitan area
(-761.3±39.7 m) is stronger than that over Taehwa (-721.7±38.6 m)
which is covered by forests instead of the urban environment. This is
consistent with CAMS' relatively better capability of capturing vertical
gradient of CO2 over Taehwa compared to that over Seoul, supporting
our previous implication of the possible inefficient BL mixing in CAMS over
the Seoul urban environment.

Figure 6Case study for the flight on 24 May (UTC time).
(a) Vertical distributions (hereafter denoted as “sections”) of
fluxes (kg m−2 s−1) at 09:00 UTC on 25 May (South Korean time) in
meridional direction. Dots represent meridional winds going from west to east
(i.e., from China to South Korea) and crosses represent meridional winds with the
opposite direction. Sizes of the dots and crosses are proportional to the
wind speed. “Sections” at the top are for CO2 fluxes and those at
the bottom are for CO fluxes. (b) “Sections” of fluxes
(kg m−2 s−1) at 09:00 UTC on 25 May (South Korean time) are in the zonal
direction. Arrows represent meridional winds. “Sections” in
panel (b) share the same color bar as panel (a).
(c) The DC-8 aircraft measurements (left column) and bias of CAMS
along the flight track over the West Sea (right column). The top row is for
CO2 and the bottom row is for CO.

3.3.2 West (Yellow) Sea

As previously mentioned, the flights over the West (Yellow) Sea are focused
on capturing pollution outflow from China. Both CO and CO2 in this
flight group are underestimated by CAMS below 900 hPa (Fig. 4). It is the
only group in which near-surface CO2 is underestimated by all the
three CAMS configurations. In addition, the underestimation of CAMS CO over
the West Sea is more significant than that over the other groups. We list two
possible reasons for this unique model performance over the West Sea
considering that the Chinese outflows constitute the dominant influence of CO
and CO2 samples in this group. First, the transport of surface
pollution from China to the West Sea is not well represented in CAMS. Second,
emissions in China may not be as well quantified as in South Korea. During
the 24 May flight, a strong outflow from China was expected, so the DC-8
aircraft flew an extended sampling “wall” over the West Sea to sample
transport from China. We show in Fig. 6 some of the details of this flight.
In particular, we show the vertical cross sections of meridional (Fig. 6a)
and zonal (Fig. 6b) fluxes of CO and CO2 in CAMS FC9s. These fluxes
are calculated as the product of meridional (from west to east) or zonal
(from south to north) wind speed with simulated species density (i.e., in
terms of units, ms-1×kgm-3=kgm-2s-1).
The China outflow moving towards the West Sea and Seoul is well demonstrated
in the fluxes of CO in Fig. 6a and b especially in the region marked by the
black rectangles. This outflow is not apparent in the fluxes of
CO2. This is because the variations in CO2 density are
very low relative to CO2 background in contrast to CO variations.
We also show in Fig. 6c the measurements from the DC-8 aircraft and the bias
of FC9s over the West Sea on that day. As can be seen in Fig. 6, CAMS
CO2 and CO are largely underestimated (CO2: 2–4 ppmv,
CO: 86–88 ppbv) for this flight. This underestimation in both species is
consistent with Fig. 4. Note that the underestimation of CO2 over
the West Sea is not consistent with other flights and the overall results.
This underestimation could be associated with an underestimation of
anthropogenic emissions in China and/or transport from China to the West Sea.
This is discussed in Sect. 3.4 in more detail. In summary, the transport
pattern of China outflow (CO and CO2) to the West Sea is captured
but the abundances of both CO and CO2 are underestimated by CAMS
especially near the surface.

Figure 7Case study for the flight on 4 June (UTC time). (a) Flight
track of DC-8 aircraft in the Seoul–Jeju jetway group for this day. The
Daesan chemical facility is marked as a black pentagram and two power plants
are marked as black triangles. Arrows correspond to 950 hPa wind field at
12:00 LT. (b) Box plot of CAMS bias from all the DC-8
aircraft measurements during the campaign (left) and from measurements on
4 June in the Seoul–Jeju jetway group (right). Top row is for CO2
and bottom row is for CO. (c) Time series of the DC-8 aircraft
measurements and CAMS during the flight. (d) The pdfs of CO and
CO2 for measurements on 4 June of the Seoul–Jeju jetway group
(solid) and for all groups (dashed).

3.3.3 Seoul–Jeju and Seoul–Busan jetways

Measurements in the Seoul–Jeju and Seoul–Busan jetways are both above the
South Korean peninsula, connecting Seoul to Jeju and Busan, respectively.
While both flight groups share some common features, they are treated here
as two distinct groups for the following reasons: (1) the Seoul–Jeju jetway is
close to the west coast of South Korea, whereas the Seoul–Busan jetway sampled
air southeast of Seoul and more inland; (2) there are more croplands, urban,
and built-up areas along Seoul–Jeju jetway while there are more forested
areas along Seoul–Busan jetway; (3) there are some important point sources
along Seoul–Jeju jetway such as power plants and the Daesan chemical
facility. In fact, the 4 June flight was designed to survey point
sources west of Seoul and focused more to the Seoul–Jeju jetway. Details of
the 4 June flight are summarized in Fig. 7. In contrast to the
overall statistics across all flight groups, FC16s, FC9s, and ANs for this
flight clearly overestimate CO near point sources. We also note that
measurements for this flight are mostly taken below 900 hPa. As such, the
spatial variations are larger near point sources than in other conditions.
Nevertheless, these variations are not well captured by CAMS, especially by
ANs. This may be due to its coarser grid representation (i.e., 40 km for
CO2 and 80 km for CO). In addition, we find a difference in terms of
mean bias in CO2 between CAMS FC9s and FC16s. This difference is not
apparent in CO. This implies there might be large spatiotemporal errors
existing in CO emission inventories in the region, since higher emission
resolution does not result in an improvement. In this case, increasing the
spatiotemporal resolution might even weaken the simulation results, whereas
lower resolution usually agrees better with observations as it “diffuses”
the error of the emissions.

Table 3Enhancement ratios of CO to CO2 (ppbv ppmv−1), CO
and CO2 correlations, and bias of CO to bias of CO2
correlations from airborne measurements, CAMS FC16s, ANs, and FC9s.

3.4 Enhancement ratios of CO to CO2

We also evaluate the three CAMS configurations against the DC-8 aircraft data
in terms of enhancement ratios of CO to CO2
(dCO∕dCO2) for all flights and for each
flight group. We conduct a reduced major axis (RMA) regression to estimate
the sensitivity of CO to CO2 (i.e.,
dCO∕dCO2) with the 1 min merges. We use
RMA instead of ordinary least squares (OLS) regression as the two variables
(CO and CO2) are both subject to error (Smith, 2009). The estimated
regression slope in the RMA corresponds to the enhancement ratio of CO and
CO2. This ratio can reflect the emission ratios of a particular
area especially when using near-field data (Parrish et al., 2002). Despite
its limitations (Yokelson et al., 2013), such analysis has been used in
previous studies for surface CO and NOx (Parrish et al.,
2002), emission factors for biomass burning (Wofsy et al., 1992; Lefer et
al., 1994; van Leeuwen and van der Werf, 2011), flask samples of CO and
CO2 in east Asia (Turnbull et al., 2011), airborne measurements of
CO and CO2 during TRACE-P (Suntharalingam et al., 2004), surface CO
and CO2 in rural Beijing (Wang et al., 2010), and more recently
with satellite retrievals of CO (MOPITT) and CO2 (GOSAT) (Silva et
al., 2013). We present our estimates of
dCO∕dCO2 (with units of
ppbv ppmv−1) from the DC-8 aircraft data and CAMS FC16s, FC9s, and ANs
in Table 3. Overall, the observed
dCO∕dCO2 during the KORUS-AQ campaign is
∼13 ppbv ppmv−1 (or ∼1.3 %). This is a relatively low
value compared to reported ratios in more polluted megacities such as
Beijing. The lowest dCO∕dCO2 among the
five flight groups is observed over Seoul (∼9 ppbv ppmv−1). The
observed dCO∕dCO2 for other groups
within South Korea ranges from ∼10 ppbv ppmv−1 (Seoul–Jeju) to
∼16 ppbv ppmv−1 (Seoul–Busan and Taehwa). Taehwa is close to
and sometimes downwind of Seoul but has higher observed
dCO∕dCO2 than Seoul. We attribute this
difference to biogenic CO sources and biospheric influence on CO2
over Taehwa. The highest dCO∕dCO2 (∼28 ppbv ppmv−1) is observed over the West Sea. This ratio is a sharp
contrast to Seoul and other flight groups over South Korea. This indicates
that the bulk combustion efficiency over Seoul is higher in Seoul than in the
China pollution outflows over the West Sea. The ratio over the West Sea is
very consistent with dCO∕dCO2 observed
over China (upwind of the West Sea) during KORUS-AQ by ARIAs
(20–100 ppbv ppmv−1. Such “combustion signature contrast” is
consistent with previous studies in the region. During TRACE-P in 2001, the
observed ratio over Japan was ∼12–17 and ∼50–100 ppbv ppmv−1 over northern China (Suntharalingam et al.,
2004). Over Shangdianzi, China, and the Tae-Ahn Peninsula (TAP), South Korea,
Turnbull et al. (2011) reported CO : CO2ff ratios (which are
derived from measurements of CO and Δ14CO2 in flask
samples taken during winter 2009/2010), of ∼47 and ∼44 ppbv ppmv−1, respectively. They also reported that the South
Korean samples from TAP have CO : CO2ff of ∼13 ppbv ppmv−1. Wang et al. (2010) reported a change in observed
dCO∕dCO2 near Beijing from
34–42 ppbv ppmv−1 in 2005–2007 to 22 ppbv ppmv−1 in 2008.
Finally, dCO∕dCO2 values derived from
satellite retrievals in 2010 indicate a similar contrast between
Beijing/Tianjin (∼25–50 ppbv ppmv−1) and Seoul (∼7–9 ppbv ppmv−1). Despite the differences in the data sources
(satellites, airborne measurements, flask samples) and time period, these
dCO∕dCO2 values are consistent and all
point to a “combustion signature contrast” between South Korea and China.
We expect that this contrast may be decreasing over time as Chinese
combustion activities become more efficient.

These observed ratios are remarkably consistent with
dCO∕dCO2 from CAMS (see Table 3). The
three CAMS configurations have dCO∕dCO2
over the Seoul metropolitan area of ∼8 to 12 ppbv ppmv−1 and
over the West Sea of ∼31–32 ppbv ppmv−1. Our rough estimates of
CO to CO2 emission ratios in CAMS over Seoul and China during
KORUS-AQ also show marked similarity with CAMS enhancement ratios. The CO to
CO2 emission ratio over China is about 28 (ppbv ppmv−1) and
about 10 (ppbv ppmv−1) over South Korea. Our results suggest that CAMS
emission ratios reflect this contrast and that the modeled
dCO∕dCO2 is indicative of emissions of
Seoul and China. To further understand the skill of CAMS in capturing this
contrast, we compare the observed correlation between CO and CO2
and the correlation from CAMS FC16s, FC9s, and ANs. This
corr(CO2,CO) is presented in the second row of
Table 3. Over Seoul, the observed corr(CO2,CO)
is moderately high (∼0.8), which is likely driven by common CO and
CO2 sources (mostly local anthropogenic emissions from Seoul). This
correlation is well captured by ANs and FC9s but not FC16s. We attribute this
difference to a better initialization in ANs and FC9s due to assimilation.
The observed corr(CO2,CO) over the West Sea is
even higher (0.89), indicating that CO and CO2 come from common
sources in China. However, this corr(CO2,CO) is
not captured by any of the three configurations (0.25–0.42). A few factors
may contribute to this low corr(CO2,CO) over the
West Sea. First, the flight on 12 May is a noteworthy source of low
corr(CO2,CO) in CAMS. We have shown in Fig. 2
that the major goal of this flight is to study AQ conditions during a frontal
passage instead of sampling China outflows. Even though part of the track
during 12 May is located in the West Sea, the AQ features of that day are
evidently different from China outflow events. After excluding measurements
during 12 May, the corr(CO2,CO) values in CAMS
(FC16s: 0.51, FC9s: 0.43, and ANs: 0.29) are now higher albeit still lower
than observed (0.9). Uncertainties in model transport can be a likely cause
as the corr(CO2,CO) can be subject to transport
errors even though dCO∕dCO2 may not
necessarily be affected. Performance of CAMS over the Baengnyeong site
(discussed in Sect. 4.1) also implies possible issues with transport of China
pollution towards the West Sea. Furthermore, the difference in temporal
representation of China emissions in CAMS may contribute to this mismatch in
timing and hence result in low correlation. As mentioned in Sect. 2, CAMS
uses prescribed monthly emission for CO while the diurnal cycle of
CO2 fluxes is calculated online in CAMS. In fact, there is a strong
diurnal cycle in the spatial correlations between CO emissions and
CO2 fluxes in CAMS caused by diurnal cycles of the CO2
NEE (Fig. S8). The diurnal cycle of spatial correlations between CO emissions
and CO2 fluxes over South Korea in CAMS peaks (∼0.7) in
daytime when measurements over South Korea were made. On the other hand,
during the nighttime, the correlations between CO emissions and CO2
fluxes in CAMS are relatively low over east China (< 0.4). This
implies that the relatively low correlations between the CO and CO2
abundances over the West Sea in CAMS may reflect the effect of nighttime
emissions from east China in CAMS. Lastly, the corr(CO2,CO) values in FC16s and FC9s are closer to observed
corr(CO2,CO) than in ANs suggesting that
resolution may also play a role. For the other three flight groups, the
observed corr(CO2,CO) values are not as high as
those over Seoul and the West Sea. This implies that CO2 and CO
observed over these three flight groups may not come from common sources
and/or have been mixed with the environment. CAMS
corr(CO2,CO) values do not always agree with
observed corr(CO2,CO). Overall,
corr(CO2,CO) from FC16s is higher than observed
while corr(CO2,CO) values from FC9s and ANs
agree well with observed corr(CO2,CO). Again,
this may be related to the fact that FC16s is generated from a free-running
simulation (i.e., not initialized with analyses).

Finally, we present the correlation between the biases of CAMS for the two
species
(corr(BiasCO,BiasCO2))
(please see the third row of Table 3). This correlation provides another
piece of information on whether the performance of CAMS in CO2 and CO
is related. We find that
corr(BiasCO,BiasCO2)
values are high over Seoul and the West Sea, indicating that the performance of
CAMS in CO and CO2 is related for the two groups. Over the West Sea,
FC16s, FC9s, and ANs perform similarly. However, the
corr(BiasCO,BiasCO2)
values are lower in the other three groups relative to Seoul and the West Sea. In
addition, our results show that ANs and FC9s usually have lower
corr(BiasCO,BiasCO2)
than FC16s, especially over Seoul. This implies that FC16s performance in
CO2 and CO is more strongly related than FC9s and ANs performance,
which could be associated again with the fact that FC16s come from a
free-running simulation while FC9s and ANs are both initialized from analyses.
The assimilation of CO and CO2 satellite retrievals may reduce the
interdependence of CAMS CO2 and CO performance.

Figure 8Comparisons of CAMS against ground site measurements. Values of CAMS
are averages across layers with pressure higher than 95 % of the surface
pressure. (a) Time series of measured and CAMS CO2 from
the Taehwa and Yonsei sites, and CO from the Baengnyeong, Fukue, Olympic Park,
and Taehwa sites. Shades denote same events as they do in Fig. 2.
(b) Box plot of CAMS bias for CO2 at the Taehwa and Yonsei
site measurements, and for CO at the Baengnyeong, Fukue, Olympic Park, and
Taehwa sites. Dates are in mm/dd format.

In this section, we evaluate CAMS FC16s and FC9s, and ANs against CO and/or
CO2 measurements from five ground sites, two ships, and four
satellites. Unlike the data from the DC-8 aircraft, data on CO2 or CO
in these cases may not be jointly available. In particular, each ground site
(except Taehwa) only measures one of the two species. The ships also provide
measurements for CO only while the four sets of satellite retrievals of
CO2 and CO are from four different instruments aboard four different
satellites. Therefore, in this section, CO2 and CO are evaluated
separately, and relationships between CO2 and CO inferred from some of
these sites are only indicative of a larger pattern that we see in the DC-8
aircraft data.

4.1 Comparison with ground observations

Here, we focus our evaluation on CAMS performance in capturing surface
conditions and diurnal cycle of CO2 and/or CO. Data from the following
five ground sites are used in this study: Baengnyeong, Fukue, Olympic Park,
Taehwa, and Yonsei University (Fig. 1 and Table 2). It can be seen in Fig. 8
that CO from Olympic Park and CO2 from Yonsei and Taehwa clearly show a
diurnal cycle during KORUS-AQ. This feature is well captured by CAMS. CO at
Taehwa, on the other hand, exhibits a very weak diurnal cycle that is not
captured by CAMS. At this site, CO in CAMS (especially ANs) shows a strong
diurnal cycle. Variations of CO in the remote sites of Baengnyeong and Fukue
also appear to be irregular and episodic. Signatures of elevated CO can
also be seen at these sites, some of which coincide with pollution
transport from China sampled by the DC-8 aircraft. The mean diurnal cycle
for these five ground sites can be found in Fig. S9.

While CAMS is able to get the observed timing of CO2, the modeled
magnitudes of CO2 (and CO) at these sites from CAMS are too high
(especially for the sites in and near Seoul). We took the average value
across a few layers near the model surface in CAMS to provide a reasonable
comparison at these sites. We use model vertical layers below 95 % of the
model surface pressure (i.e., if surface pressure is 1000 hPa, we average the
layers below 950 hPa) to account for potential weak BL mixing (especially
near source regions). This feature in CAMS has been discussed in Sect. 3.3.1. Since this averaging may introduce errors in our comparison, we only
evaluate CAMS in terms of relative patterns (diurnal cycle and spatial
variability across sites). Note that CAMS CO along the ship tracks (to be
discussed in the successive section) is also averaged across a few layers
in the same way for consistency. We show in Fig. 8 the summary statistics of
the bias in CAMS relative to ground observations. The box plots show that the
variability of model bias in CO is in general smaller for remote sites and
larger for the two sites in the Seoul metropolitan area. The bias in CAMS is also
smaller in Fukue than in Baengnyeong, where a larger influence of pollution
transport from China is observed but not well captured in CAMS. It is also
worth mentioning that relative to other sites, CAMS significantly
overestimates both CO and CO2 at Taehwa. This may be due to the
proximity of Taehwa to Seoul. The model grid spacing may not be able to
resolve well the subgrid-scale processes (emissions) and variations between
Seoul and Taehwa. This overestimation is most apparent in CAMS ANs which has
a coarser grid spacing (40 km for CO2 and 80 km for CO) than FC16s and
FC9s. In the case of CO2 at Yonsei, we find lower bias in CAMS FC9s and
ANs than FC16s suggesting improvements of CAMS due to better initialization.

Figure 9Comparisons of CAMS CO against ship measurements. Values of CAMS are
averages across layers with pressure higher than 95 % of the surface
pressure. (a) Bias of CAMS CO against ship measurements along the
ship track. (b) Box plot of CAMS bias for CO compared with ship
measurements.

We take advantage of the location of the sites in Olympic Park (CO) and
Yonsei University (CO2) which are within the Seoul metropolitan area and the
collocated measurements of CO and CO2 in Taehwa to investigate
patterns of ground-based dCO∕dCO2 in
Seoul and Taehwa. Here, we only discuss observed
dCO∕dCO2 since the modeled
dCO∕dCO2 at these ground sites may not
be accurate given CAMS issues with vertical mixing near the surface and
representativeness errors. Following similar analysis with the
dCO∕dCO2 of the DC-8 aircraft data,
regressions of CO to CO2 at these sites can represent emission
ratios of CO to CO2 in the Seoul metropolitan area. Our estimate of
dCO∕dCO2 from the Olympic Park and Yonsei
sites is 11.32 ppbv ppmv−1. This is consistent with
dCO∕dCO2 calculated from the DC-8
aircraft data which sampled air closely above these sites (∼9 ppbv ppmv−1). Our estimate of
dCO∕dCO2 from the Taehwa site is
6.57 ppbv ppmv−1. This is different from our estimate of
15.3 ppbv ppmv−1 based on the DC-8 aircraft data. Unlike Seoul,
70 % of the airborne measurements over Taehwa are taken above 800 hPa.
Over Taehwa, airborne dCO∕dCO2 varies
with altitude from 8.92 ppbv ppmv−1 below 950 hPa,
10.28 ppbv ppmv−1 below 900 hPa, and 14.74 ppbv ppmv−1 above
400 hPa.

Figure 10Spatial distributions of CAMS bias against satellite retrievals. For
XCO, the unit is 1018 molecules cm−2, while for XCO2,
the unit is 1021 molecules cm−2.

4.2 Comparison with ship observations

Two research vessels (Jangmok and Onnuri) were deployed during KORUS-OC. The
two ships traveled along the South Korean coast and measured CO from 20 May
to 5 June (as marked in Fig. 1). Measurements of CO from
ships and biases of CAMS FC16s, ANs, and FC9s are shown in Fig. 9. Note
that CAMS values along ship tracks are also averaged across a few layers
near the surface in the same way CAMS at ground sites was processed. CAMS at
three (out of four) ground sites tend to underestimate CO, while CAMS
overestimates CO relative to ship measurements. This seems to be
inconsistent with our findings with airborne measurements (i.e., CO is
underestimated by CAMS at the lowermost troposphere (Figs. 4 and 6). This is
likely due to the differences in sampling between the airborne and ship
measurements. Over sea, the DC-8 aircraft often sampled air from China
outflow while the two ships continuously sampled air over the waters
regardless of the presence of China outflows. The ship measurements reflect
surface conditions over waters which may also be different from what is
observed by the DC-8 aircraft along the vertical profile. This inconsistency
is further discussed in the next section with satellite data.

4.3 Comparison with satellite retrievals

The total column dry-air mole fractions of CO2 and CO
(XCO2 and XCO) derived from CAMS are compared here to
XCO2 from OCO-2 and GOSAT, and XCO from MOPITT and IASI. It is
worth noting that satellite retrievals may have associated bias and
uncertainties, which are generally larger than those of ground and airborne
measurements. Slight inconsistencies also exist between MOPITT XCO and IASI
XCO (George et al., 2009, 2015). We show in Fig. 10 the spatial distribution
of CAMS biases against these retrievals. We also summarize the statistics in
Table 4. Overall, ANs tend to agree better with satellite observations than
the forecasts. For CO, CAMS XCO tends to be higher than MOPITT but lower than
IASI. In addition, CAMS XCO agrees better with MOPITT than IASI. For
CO2, CAMS XCO2 tends to be higher than GOSAT but lower
than OCO-2. FC16s, FC9s, and ANs differ from each other in terms of bias when
compared to any of the four satellite retrievals although there is no clear
difference in terms of RMSE. For XCO, when compared to MOPITT, ANs are better
than the two forecasts in terms of bias, RMSE, and correlation. When compared
to IASI, ANs are better in terms of correlation but not bias. For
XCO2, ANs do not show improvements from the two forecasts when
compared to both OCO-2 and GOSAT retrievals. For both XCO and XCO2,
FC9s are not necessarily better than FC16s. In summary, ANs XCO shows better
agreement with satellite retrievals but this is not the case for
XCO2. Differences in the resolution and amount of satellite data of
XCO and XCO2 could be two possible causes. The spatial and temporal
resolutions of FC16s and FC9s are higher than those of ANs while ANs
assimilate observational data from these satellite retrievals (except OCO-2).
These two factors compete against each other. Because the amount of CO data
(13 612 retrievals for MOPITT and 25 509 for IASI over our study domain
during KORUS-AQ) is much larger than that of CO2 (42 for GOSAT over
our domain during KORUS-AQ), there are more observational constraints for CO
in CAMS resulting in better performance of ANs CO (Fig. 9 and Table 4). The
opposite is the case for CO2. The model resolution dominates for
CAMS CO2 performance especially with regards to capturing
spatiotemporal variability. Scatter plots of CAMS XCO and XCO2
against satellite observations are also presented in Fig. S10 of the
Supplement.

We note that CAMS overestimates XCO when compared with MOPITT XCO over the
West Sea (Fig. 10). This appears to be contradictory to our conclusions in
Sect. 3 and the similar inconsistency also exists when we compare CAMS CO
with ship measurements (as mentioned in Sect. 4.2). To further explain this
inconsistency, we compare CAMS FC9s with ship measurements and satellite XCO.
Because the West Sea flight group in the DC-8 aircraft data forms a zonal
“wall” and such measurements over the West Sea are only conducted when a
China outflow is expected, we separate the days when China outflows are
present. The following are the days during the campaign when China outflows
were expected to occur and DC-8 flights measured walls over the West Sea: 3,
17, 24, 29, and 30 May. On 3, 17, 24, and 29 May, there are no MOPITT
observations over the West Sea (Fig. S11). Therefore, the overall differences
between CAMS FC9s and MOPITT observations are driven by the non-outflow days.
On 30 May, however, there are MOPITT observations over the West Sea. Unlike
the overall picture (Fig. 10), we find that CAMS actually underestimates the
outflows over the West Sea on that day, which is consistent with our findings
in Sect. 3. On 1 June (a non-China outflow day), comparison with ship
measurements indicates that CAMS FC9s overestimate CO near the South Korean coast.
It is also consistent with MOPITT XCO on 1 June (Fig. S11). This
overestimation in CAMS FC9s is also captured in our comparison with
Baengnyeong (highlighted by a black box in Fig. 9). We find similar
overestimation using CAMS FC16s and ANs. Hence, during “normal” conditions,
CAMS tend to overestimate CO over the West Sea, whereas during China outflow
events, CAMS tends to underestimate CO. More elaborate analysis of source
contributions during KORUS-AQ is beyond the scope of this study and can be
found in Tang et al. (2018), who suggested that during China outflow
events, the contribution from Chinese direct emissions to CO over the West
Sea is largely enhanced and dominant.

We use measurements from the NASA DC-8 aircraft, five ground sites
(Baengnyeong, Fukue, Olympic Park, Taehwa, and Yonsei University), and two
R/Vs (Jangmok and Onnuri) during the KORUS-AQ field campaign, along with
four sets of satellite retrievals (MOPITT XCO, IASI XCO, OCO-2 XCO2,
and GOSAT XCO2) to evaluate the capability of a high-resolution global
modeling system (CAMS) in simulating anthropogenic combustion. Specifically,
we evaluate the performance of CAMS FC16s, FC9s, and ANs of CO2, CO,
and their relationships. Our assessment of the overall performance of CAMS
against the DC-8 aircraft data show that (1) the nominal background
CO2 in CAMS is slightly overestimated (bias is 2.2 ppmv for FC16s, 0.7 ppmv for FC9s, and 0.3 ppmv for ANs), which is further improved by CO2
analysis. On the other hand, CO is generally underestimated by CAMS (bias is
−19.2 ppbv for FC16s, −16.7 ppbv for FC9s, and −20.7 ppbv for ANs); and (2) among the three forecasts/analysis configurations, FC9s are more accurate
and consistent overall than FC16s and ANs because of the finer model
resolution and improved initialization. While ANs are coarser in resolution,
they generally perform better than FC16s as the impact of initialization
surpasses the impact of resolution (Fig. S3). We also classify the airborne
measurements into five groups based on land cover below the flight tracks
and associated pollution sources. While CO2, CO, and their
relationships vary across these five groups, CAMS performs well in terms of
simulating regional pattern of anthropogenic combustion. This is because (1) CAMS simulations of both species have relatively low bias; and (2) CAMS
reproduces dCO∕dCO2 observed by the DC-8
aircraft. Both CAMS and the DC-8 aircraft data show more efficient
combustion (low dCO∕dCO2) over Seoul than
over the West Sea which is representative of Chinese outflows. Our case
study on the 24 May flight over the West Sea indicates that
the Chinese outflow is captured by CAMS. However, the modeled CO and
CO2 concentrations are significantly underestimated (by −2 to −4 ppmv
for CO2 and −86 to −88 ppbv for CO) especially within the lowermost
troposphere. This suggests that, although CAMS emission ratios are
relatively consistent with dCO∕dCO2, the
absolute magnitudes of China emissions are still underestimated. CAMS also
shows poorer performance at local-to-urban scales as exemplified by our case
study on the 4 June flight where larger variations near point sources
were not represented in CAMS. Our comparisons with measurements from ground
sites and two ships indicate that (1) the diurnal cycles of CO and CO2
are stronger over urban environments and such periodic features are
reasonably captured by CAMS; (2) vertical mixing near sources (such as
Seoul) is too weak in CAMS and needs to be improved; and (3) in some cases,
FC9s do not show improvements from FC16s (such as over Seoul and the point
sources during the 4 June flight), implying large spatiotemporal
errors in emission inventories. In these cases, increasing the
spatiotemporal resolution might even weaken the simulation results, whereas
lower resolution usually agrees better with observations as it “diffuses”
the error of the emissions. We also compared XCO and XCO2 derived from
CAMS to satellite retrievals from four instruments (MOPITT CO, IASI CO,
OCO-2 CO2, and GOSAT CO2). We find that ANs XCO shows better
agreement with satellite retrievals compared to the forecasts, while ANs
CO2 is no better than the forecasts. We attribute this contrast to
significant differences in the number of XCO and XCO2 satellite data
potentially available for assimilation.

We recognize the following limitations of this work. (1) The temporal
distributions of airborne measurements are not completely independent from
their spatial distributions. For example, most of the measurements in the
West Sea group are conducted before noon, whereas measurements in Seoul–Busan
jetway are concentrated in the afternoon. (2) CAMS is only evaluated over the
South Korean peninsula and surrounding waters during the campaign (1 May to
10 June). More work is needed to determine if our findings are valid over
other regions. For example, Agustí-Panareda et al. (2014) reported the
overall overestimation of CO2 in spring over the whole NH and it is
enhanced by biogenic flux correction. (3) Inconsistencies exist even among
different satellite products (George et al., 2009, 2015), thus limiting our
comparisons with CAMS to relative differences; and (4) our comparisons of CAMS
with ground and ship measurements are only qualitative and indicative as CAMS
surface concentrations are significantly higher than surface observations and
not comparable.

Finally, this study has important implications on the design and
implementation of current and future prediction systems for atmospheric
composition and air quality. Although CAMS captured the regional combustion
signatures, it still has difficulty representing the variability at
local-to-urban scales even at finer resolution. This suggests the need for
improvements in both observational constraints and model representation of
relevant processes (e.g., emissions and BL mixing).

This work is supported by NASA KORUS-AQ
(NNX16AE16G and NNX16AD96G). We thank the KORUS-AQ team for observational
data, the CAMS global production team for the model products of CO and
CO2, MOPITT, IASI, OCO-2, and GOSAT data teams for satellite data. IASI
CO is provided by LATMOS/CNRS and ULB. We acknowledge NASA and the OCO-2
project for OCO-2 CO2 data. We thank the DIAL-HSRL team for the mixed
layer heights product. The authors thank Cenlin He and Kazuyuki Miyazaki for helpful comments on improving the paper. NCAR is sponsored by
the National Science Foundation. Yugo Kanaya was supported by the
Environment Research and Technology Development Fund (2-1505 and 2-1803) of
the Ministry of the Environment, Japan. The authors thank the anonymous
reviewers for their comments and suggestions. The CAMS data were generated
using Copernicus Atmosphere Monitoring Service Information (2016).